In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
Make sure that you've downloaded the required human and dog datasets:
Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location /dogImages.
Download the human dataset. Unzip the folder and place it in the home directory, at location /lfw.
Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.
# Import libraries
import os
from time import time
from glob import glob
import numpy as np
import pandas as pd
from collections import Counter
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style('white')
import cv2
import torch
import torchvision.transforms as transforms
import torchvision.models as models
import torch.optim as optim
from torchvision import datasets
import torch.nn as nn
import torch.nn.functional as F
from sklearn.metrics import accuracy_score
# the following import is required for training to be robust to truncated images
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
#from tqdm.notebook import tqdm
from tqdm import tqdm
# check if CUDA is available
use_cuda = torch.cuda.is_available()
if use_cuda:
print('Cuda is available')
#from workspace_utils import active_session
# load filenames for human and dog images
human_files = np.array(glob("data/lfw/*/*"))
dog_files = np.array(glob("data/dog_images/*/*/*"))
# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.
OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[0])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
Question 1: Use the code cell below to test the performance of the face_detector function.
human_files have a detected human face? dog_files have a detected human face? Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer:
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
#-#-# Do NOT modify the code above this line. #-#-#
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
def face_detection_test(images):
c = 0
l = len(images)
for img in images:
c += face_detector(img)
return c, l
c_human, l_human = face_detection_test(human_files_short)
c_dog, l_dog = face_detection_test(dog_files_short)
print(f'{c_human} out of {l_human} or about {c_human/l_human:.0%} was correctly detected faces in human images')
print(f'{c_dog} out of {l_dog} or about {c_dog/l_dog:.0%} was uncorrectly detected faces in dog images')
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
### (Optional)
### TODO: Test performance of another face detection algorithm.
### Feel free to use as many code cells as needed.
In this section, we use a pre-trained model to detect dogs in images.
The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.
# define VGG16 model
VGG16 = models.vgg16(pretrained=True)
# move model to GPU if CUDA is available
if use_cuda:
VGG16 = VGG16.cuda()
Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.
In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.
Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.
# Set PIL to be tolerant of image files that are truncated.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def VGG16_predict(img_path):
'''
Use pre-trained VGG-16 model to obtain index corresponding to
predicted ImageNet class for image at specified path
Args:
img_path: path to an image
Returns:
Index corresponding to VGG-16 model's prediction
'''
## TODO: Complete the function.
## Load and pre-process an image from the given img_path
## Return the *index* of the predicted class for that image
# First load the image
img = Image.open(img_path) #.convert('RGB')
# Setup the normalizer
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# Setting up image preprocessor
img_transform = transforms.Compose([transforms.Resize((224, 224)), # Resize the image to 244x244
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
# Apply the preprocessing to the image before passing it to our model
# and add a dummy axis as the model expect a batch of images not a single image
preproccessed_img = img_transform(img)[:3,:,:].unsqueeze(0)
# check if CUDA is available
use_cuda = torch.cuda.is_available()
# move imag to GPU if CUDA is available
if use_cuda:
preproccessed_img = preproccessed_img.cuda()
# Use VGG16 to predict the class of the image
pred = VGG16(preproccessed_img)
# Move the prediction to cpu and convert it to numpy array and return the index of the class of highest probability
pred = pred.cpu().data.numpy().argmax()
return pred # predicted class index
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).
Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
## TODO: Complete the function.
# Use VGG16_predict to get the class index
pred_idx = VGG16_predict(img_path)
# Return True if the class is between 151 and 268 inclusive
result = pred_idx >=151 and pred_idx <=268
return result # true/false
Question 2: Use the code cell below to test the performance of your dog_detector function.
human_files_short have a detected dog? dog_files_short have a detected dog?Answer:
# Helper function to test the dog detector
def dog_detection_test(images):
c = 0
l = len(images)
for img in images:
if dog_detector(img):
c += 1
return c, l
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
# Test the model in human images
detected_human, len_human = dog_detection_test(human_files_short)
# Test the model in dog images
detected_dog, len_dog = dog_detection_test(dog_files_short)
# Print the results
print(f'{detected_human} out of {len_human} or about {detected_human/len_human:.0%} was uncorrectly detected dogs in human images')
print(f'{detected_dog} out of {len_dog} or about {detected_dog/len_dog:.0%} was correctly detected dogs in dog images')
We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
### (Optional)
### TODO: Report the performance of another pre-trained network.
### Feel free to use as many code cells as needed.
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
![]() |
![]() |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
![]() |
![]() |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
![]() |
![]() |
![]() |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!
### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
# define training , validation and test data directories
data_dir = 'data/dog_images/'
train_dir = os.path.join(data_dir, 'train/')
valid_dir = os.path.join(data_dir, 'valid/')
test_dir = os.path.join(data_dir, 'test/')
# Setup the normalizer
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# Transforming the data and applying image augmentation for training dataset
train_transform = transforms.Compose([transforms.Resize((224, 224)), # Resize the image to 256x256
transforms.RandomRotation(30), # Randomly rotate the image in the range of 30 degree
transforms.RandomHorizontalFlip(), # Randomly flip the image horizontally
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
# Transformation for test and validation datasets
val_test_transform = transforms.Compose([transforms.Resize((224, 224)), # # Resize the image to 256x256
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
train_data = datasets.ImageFolder(train_dir, transform=train_transform)
valid_data = datasets.ImageFolder(valid_dir, transform=val_test_transform)
test_data = datasets.ImageFolder(test_dir, transform=val_test_transform)
# print out some data stats
print(f'Number of training images: {len(train_data)}')
print(f'Number of validation images: {len(valid_data)}')
print(f'Number of test images: {len(test_data)}')
# define dataloader parameters
batch_size = 20
num_workers=0
# prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size,
num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size,
num_workers=num_workers)
loaders_scratch = {'train': train_loader,
'valid': valid_loader,
'test': test_loader}
# Data frame for number of classes accros train dataset
num_train_classes = pd.DataFrame.from_dict(dict(Counter(train_data.targets)), orient='index').reset_index()
num_train_classes.columns = ['class', 'counts']
# Data frame for number of classes accros validation dataset
num_val_classes = pd.DataFrame.from_dict(dict(Counter(valid_data.targets)), orient='index').reset_index()
num_val_classes.columns = ['class', 'counts']
# Data frame for number of classes accros test dataset
num_test_classes = pd.DataFrame.from_dict(dict(Counter(test_data.targets)), orient='index').reset_index()
num_test_classes.columns = ['class', 'counts']
# Display Datasets summary statistics
print(f'Train Dataset\n{num_train_classes["counts"].describe()}\n\n\
Validation Dataset\n{num_val_classes["counts"].describe()}\n\n\
Test Dataset\n{num_test_classes["counts"].describe()}')
# Helper function to plot the distribution accross classes
def plot_num_classes(dfs_list=[num_train_classes, num_val_classes, num_test_classes], colors=['#00334e', '#40e580']):
"""Ploting the distribution of classes count"""
# List for plot title
titles = ['Train Dataset', 'Validation Dataset', 'Test Dataset']
# Create figure with 2x2 subplots
fig, axis = plt.subplots(figsize=(20, 20), nrows=3)
# Plotting loops
# Iterate over axis
for i, ax in enumerate(axis.flatten()):
med = dfs_list[i]['counts'].median()
ax.plot(dfs_list[i]['counts'], color=colors[1])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.axhline(y=med, color=colors[0])
ax.text(x=133/2, y=1.02*med, s=f'Median = {med:.0f}', fontdict={'fontsize': 20, 'color':colors[0]})
ax.set_title(titles[i] + ' Classes Ditribution', fontdict={'fontsize': 22, 'fontweight':'bold'})
ax.set_xlabel('Class Number', fontdict={'fontsize': 14})
ax.set_ylabel('Count', fontdict={'fontsize': 14})
ax.set_xticks(np.arange(0, 133, 5))
ax.set_xlim(0)
ax.set_ylim(0);
plot_num_classes()
Question 3: Describe your chosen procedure for preprocessing the data.
Answer:
Create a CNN to classify dog breed. Use the template in the code cell below.
# define the CNN architecture
class Net(nn.Module):
### TODO: choose an architecture, and complete the class
def __init__(self):
super(Net, self).__init__()
## Define layers of a CNN
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
self.conv4 = nn.Conv2d(128, 128, 3, padding=1)
self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
self.conv6 = nn.Conv2d(256, 256, 3, padding=1)
# Maxpooling layer
self.pool = nn.MaxPool2d(2, 2)
# Fully connected layers
self.fc1 = nn.Linear(7*7*256, 500)
self.fc2 = nn.Linear(500, 133)
# Dropout layer
self.dropout = nn.Dropout(0.50)
# Batch Normalizing layers for conv layers
self.conv_bn1 = nn.BatchNorm2d(128)
self.conv_bn2 = nn.BatchNorm2d(256)
# Batch Normalizing layer for fully connected layers
self.bn = nn.BatchNorm1d(500)
def forward(self, x):
## Define forward behavior
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.dropout(x)
x = self.pool(F.relu(self.conv_bn1(self.conv3(x))))
x = self.pool(F.relu(self.conv4(x)))
x = self.dropout(x)
x = self.pool(F.relu(self.conv_bn2(self.conv5(x))))
x = self.dropout(x)
x = F.relu(self.conv_bn2(self.conv6(x)))
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = F.relu(self.bn(self.fc1(x)))
x = self.dropout(x)
x = self.fc2(x)
return x
#-#-# You do NOT have to modify the code below this line. #-#-#
# instantiate the CNN
model_scratch = Net()
# move tensors to GPU if CUDA is available
use_cuda = torch.cuda.is_available()
if use_cuda:
model_scratch.cuda()
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.
Answer:
For the problem we have here in order to acheive the best accuracy I would have chosed a deeper neural network with much more layers, but for the purpose of this task I chosed the following:
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.
# Weight tensor for CrossEntropyLoss
weight = torch.FloatTensor(1/num_train_classes['counts']).cuda()
weight
### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss(weight=weight)
### TODO: select optimizer
optimizer_scratch = optim.Adam(model_scratch.parameters(), lr=0.0001, weight_decay=0.01)
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.
# Train function
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, model_path, summary_path):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf
# Initialize empty lists to track training and validation losses and accuracy for each epoch
train_losses = []
valid_losses = []
train_acc = []
valid_acc = []
# Start time to calculate the time for training
train_start = time()
for epoch in range(1, n_epochs+1):
with tqdm(total = len(train_data)) as t_epoch_pbar:
t_epoch_pbar.set_description(f'Epoch {epoch}/{n_epochs}')
# Start time for epoch
epoch_start = time()
# initialize variables to monitor training and validation loss
train_loss = 0.0
valid_loss = 0.0
train_correct = 0.0
train_total = 0.0
valid_correct = 0.0
valid_total = 0.0
###################
# train the model #
###################
model.train()
for batch_idx, (data, target) in enumerate(loaders['train']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
## find the loss and update the model parameters accordingly
# clear the gradients of all optimized variables
optimizer.zero_grad()
# forward pass
output = model(data)
# calculate the batch loss
loss = criterion(output, target)
# backward pass
loss.backward()
# perform a single optimization step to update model parameters
optimizer.step()
# update training loss
train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
train_correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
train_total += data.size(0)
# Update the progress bar
desc = f'Epoch: {epoch}/{n_epochs} - Train loss = {train_loss:.4f} - Train Accuracy = {train_correct/train_total:.2%}'
t_epoch_pbar.set_description(desc)
t_epoch_pbar.update(data.shape[0])
######################
# validate the model #
######################
model.eval()
with tqdm(total = len(valid_data)) as v_epoch_pbar:
v_epoch_pbar.set_description(f'Epoch {epoch}/{n_epochs}')
for batch_idx, (data, target) in enumerate(loaders['valid']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
## update the average validation loss
# forward pass
output = model(data)
# calculate the batch loss
loss = criterion(output, target)
# update average validation loss
valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
valid_correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
valid_total += data.size(0)
# Update the progress bar
desc = f'Epoch: {epoch}/{n_epochs} - Valid loss = {valid_loss:.4f} - Valid Accuracy = {valid_correct/(valid_total+1e-10):.2%}'
v_epoch_pbar.set_description(desc)
v_epoch_pbar.update(data.shape[0])
# calculate average losses
# train_loss = train_loss/len(loaders['train'].dataset)
# valid_loss = valid_loss/len(loaders['valid'].dataset)
# print training/validation statistics
# print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}\n\
# Train Accuracy: {:.2f}% ({:.0f}/{:.0f})\tVal Accuracy: {:.2f}% ({:.0f}/{:.0f})\tFinished in: {:.0f} Seconds'.format(
# epoch,
# train_loss,
# valid_loss,
# 100. * train_correct / train_total,
# train_correct,
# train_total,
# 100. * valid_correct / valid_total,
# valid_correct,
# valid_total,
# time()-epoch_start
# ))
# Add train and valid loss for each epoch to the train_losses and valid_losses lists
train_losses.append(train_loss.cpu().numpy())
valid_losses.append(valid_loss.cpu().numpy())
train_acc.append(100. * train_correct / train_total)
valid_acc.append(100. * valid_correct / valid_total)
## TODO: save the model if validation loss has decreased
if valid_loss <= valid_loss_min:
print(f'Validation loss decreased ({valid_loss_min:.6f} --> {valid_loss:.6f}). Saving the model ...')
torch.save(model.state_dict(), model_path)
valid_loss_min = valid_loss
# Save Model Summary to a pandas DF
print('Saving the model summary ...')
summary_dict = {
'epoch': np.arange(1, n_epochs+1, 1),
'train_losses': train_losses,
'valid_losses': valid_losses,
'train_acc': train_acc,
'valid_acc': valid_acc,
}
model_summary = pd.DataFrame(summary_dict)
# Save the df to csv file
model_summary.to_csv(summary_path, index=False)
print(f'Finished Training in: {time()-train_start:.0f} Seconds')
# return trained model and the model_summary dataframe
return model, model_summary
# train the model
model_scratch, model_summary = train(100, loaders_scratch, model_scratch, optimizer_scratch,
criterion_scratch, use_cuda, 'models/model_scratch.pt', 'models/model_scratch.csv')
# Helper function to plot the model summary
def plot_model_summary(model_summary):
fig, ax = plt.subplots(figsize=(20, 6), ncols=2)
ax[0].plot(model_summary.train_losses, color='#40e580')
ax[0].plot(model_summary.valid_losses, color='#00334e')
ax[0].spines['top'].set_visible(False)
ax[0].spines['right'].set_visible(False)
#ax[0].spines['left'].set_visible(False)
ax[0].set_title('Training VS Validation Loss', fontdict={'fontsize': 20, 'fontweight':'bold'})
ax[0].set_xlabel('Epoch', fontdict={'fontsize': 14})
ax[0].set_ylabel('Loss', fontdict={'fontsize': 14})
ax[0].set_ylim(0)
ax[0].legend(['Training', 'Validation']);
ax[1].plot(model_summary.train_acc, color='#40e580')
ax[1].plot(model_summary.valid_acc, color='#00334e')
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)
#ax[1].spines['left'].set_visible(False)
ax[1].set_title('Training VS Validation Accuracy', fontdict={'fontsize': 20, 'fontweight':'bold'})
ax[1].set_xlabel('Epoch', fontdict={'fontsize': 14})
ax[1].set_ylabel('Accuracy', fontdict={'fontsize': 14})
ax[1].set_ylim(0)
ax[1].legend(['Training', 'Validation']);
model_summary = pd.read_csv('models/model_scratch.csv')
# Plot model scratch summary
plot_model_summary(model_summary)
# load the model that got the best validation loss
model_scratch.load_state_dict(torch.load('models/model_scratch.pt'))
# instantiate the CNN
model_scratch_SGD = Net()
# move tensors to GPU if CUDA is available
if use_cuda:
model_scratch_SGD.cuda()
# Select loss function for model scratch with SGD with momentum optimizer
criterion_scratch_SGD = nn.CrossEntropyLoss(weight=weight)
# Select optimizer
optimizer_scratch_SGD = optim.SGD(model_scratch_SGD.parameters(), lr=0.01, momentum=0.9, weight_decay=0.01)
# train the model
model_scratch_SGD, model_summary_SGD = train(100, loaders_scratch, model_scratch_SGD, optimizer_scratch_SGD,
criterion_scratch_SGD, use_cuda, 'models/model_scratch_SGD.pt', 'models/model_scratch_SGD.csv')
model_summary_SGD = pd.read_csv('models/model_scratch_SGD.csv')
# load the model that got the best validation accuracy
model_scratch_SGD.load_state_dict(torch.load('models/model_scratch_SGD.pt'))
# Plot model scratch with SGD summary
plot_model_summary(model_summary_SGD)
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.
def test(loaders, model, criterion, use_cuda):
# monitor test loss and accuracy
test_loss = 0.
correct = 0.
total = 0.
model.eval()
for batch_idx, (data, target) in enumerate(loaders['test']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
total += data.size(0)
print('Test Loss: {:.6f}\n'.format(test_loss))
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
100. * correct / total, correct, total))
# call test function
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
# Test Model SGD
test(loaders_scratch, model_scratch_SGD, criterion_scratch_SGD, use_cuda)
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).
If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.
## TODO: Specify data loaders
# define training , validation and test data directories
data_dir = 'data/dog_images/'
train_dir = os.path.join(data_dir, 'train/')
valid_dir = os.path.join(data_dir, 'valid/')
test_dir = os.path.join(data_dir, 'test/')
# Setup the normalizer
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# Transforming the data and applying image augmentation for training dataset
train_transform = transforms.Compose([transforms.Resize((224, 224)), # Resize the image to 256x256
transforms.RandomRotation(30), # Randomly rotate the image in the range of 30 degree
transforms.RandomHorizontalFlip(), # Randomly flip the image horizontally
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
# Transformation for test and validation datasets
val_test_transform = transforms.Compose([transforms.Resize((224, 224)), # # Resize the image to 256x256
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
train_data = datasets.ImageFolder(train_dir, transform=train_transform)
valid_data = datasets.ImageFolder(valid_dir, transform=val_test_transform)
test_data = datasets.ImageFolder(test_dir, transform=val_test_transform)
# print out some data stats
print(f'Number of training images: {len(train_data)}')
print(f'Number of validation images: {len(valid_data)}')
print(f'Number of test images: {len(test_data)}')
# define dataloader parameters
batch_size = 20
num_workers=0
# prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size,
num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size,
num_workers=num_workers)
## TODO: Specify data loaders
loaders_transfer = {'train': train_loader,
'valid': valid_loader,
'test': test_loader}
# Get the class names for plotting
class_names = [item[4:].replace("_", " ") for item in train_data.classes]
# Get a batch of training data
inputs, classes = next(iter(loaders_transfer['train']))
# Plot a batch of preprocessed images
fig, axis = plt.subplots(figsize=(20, 8), nrows=3, ncols=7, tight_layout=True)
axis = axis.flatten()
i = 0
for ax, label in zip(axis, classes):
image = inputs[i].to("cpu").clone().detach() # Colne the input tensor and move it to cpu
image = image.numpy().squeeze() # Convert tensot to numpy array and squeez the 1st dimintion
image = image.transpose(1, 2, 0) # Reshape the image array to (height, width, channels)
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406)) # Retain the colors again
image = image.clip(0, 1) # Clip the image values between 0 and 1
# Display the images
ax.imshow(image)
ax.set_title(class_names[label])
ax.set_xticks([]); ax.set_yticks([])
i+=1
# Delete the last axis in the figure
fig.delaxes(axis[20]);
Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.
## TODO: Specify model architecture
# define VGG16 model
model_transfer = models.vgg16(pretrained=True)
print(model_transfer)
# Change the numper of output features for the final layer to the number of classes
model_transfer.classifier[6] = nn.Linear(4096, 133)
# Freeze training for all "features" layers
for param in model_transfer.features.parameters():
param.requires_grad = False
# check if CUDA is available
use_cuda = torch.cuda.is_available()
# move model to GPU if CUDA is available
if use_cuda:
model_transfer = model_transfer.cuda()
print(model_transfer)
for p in model_transfer.parameters():
if p.requires_grad:
print(p.name, p.data.shape)
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.
dict_c = {}
for dir in os.listdir('/data/dog_images/train'):
class_num = dir.split('.')[0]
img_count = len(os.listdir('/data/dog_images/train/' + dir))
dict_c[int(class_num)] = img_count
num_train_classes = pd.DataFrame.from_dict(dict_c, orient='index').reset_index()
num_train_classes.columns = ['class', 'counts']
num_train_classes = num_train_classes.sort_values('class').reset_index()
# Weight tensor for CrossEntropyLoss
weight = torch.FloatTensor(1/num_train_classes['counts']).cuda()
criterion_transfer = nn.CrossEntropyLoss(weight=weight)
optimizer_transfer = optim.SGD(model_transfer.classifier.parameters(), lr= 0.01)
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.
# train the model
n_epochs = 100
model_transfer, model_transfer_summary = train(n_epochs, loaders_transfer, model_transfer,
optimizer_transfer, criterion_transfer, use_cuda,
'models/model_transfer.pt', 'models/model_transfer.csv')
# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load('models/model_transfer.pt'))
model_transfer.load_state_dict(torch.load('models/model_transfer.pt'))
model_transfer_summary = pd.read_csv('models/model_transfer.csv')
plot_model_summary(model_transfer_summary)
# Helper function to comparer models summary
def plot_compare_models_summary(dfs_list=[model_summary, model_summary_SGD, model_transfer_summary],
legend_list=['Scratch Model', 'Scratch SGD', 'Transfer Model'],
colors=['#40e580', '#00334e', '#e52060']):
"""Ploting the training summary of models"""
# List for plot title
titles = ['Training Loss', 'Validation Loss', 'Training Accuracy', 'Validation Accuracy']
# List for y labels
ylabels = ['Loss', 'Loss', 'Accuracy', 'Accuracy']
# List for columns to plot
cols = dfs_list[0].columns
# Create figure with 2x2 subplots
fig, axis = plt.subplots(figsize=(20, 12), nrows=2, ncols=2)
# Plotting loops
# Iterate over axis
for i, ax in enumerate(axis.flatten()):
# Iterate over dfs
for c, df in enumerate(dfs_list):
ax.plot(df[cols[i+1]], color=colors[c])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_title(titles[i], fontdict={'fontsize': 20, 'fontweight':'bold'})
ax.set_xlabel('Epoch', fontdict={'fontsize': 14})
ax.set_ylabel(ylabels[i], fontdict={'fontsize': 14})
ax.set_ylim(0)
ax.legend(legend_list);
fig.text(.3, 0.95, 'Training and Validation Loss and Accuracy', fontdict={'fontsize':24, 'fontweight':'bold'})
plot_compare_models_summary(dfs_list=[model_summary, model_summary_SGD, model_transfer_summary],
legend_list=['Scratch Model', 'Scratch SGD', 'Transfer Model'],
colors=['#40e580', '#00334e', '#e52060'])
# Test function to test the accuracy of the model over each class
def test_classes(loaders, model, criterion, use_cuda):
# monitor test loss
test_loss = 0.
# Monitor Target and predictions
targets = []
predictions = []
model.eval()
for batch_idx, (data, target) in enumerate(loaders['test']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# Append batch predictions and targets to predictions and targets lists
predictions.append(pred.cpu().numpy())
targets.append(target.data.view_as(pred).cpu().numpy())
# Concatenate the lists of numpy arrays to 1d numpy array
predictions = np.concatenate(predictions, axis=0).squeeze()
targets = np.concatenate(targets, axis=0).squeeze()
# Compare targets and predictions
results = predictions == targets
# Create dictionary for the data frame
df_dict = {
'target':targets.tolist(),
'predicted':predictions.tolist(),
'result':results.tolist()
}
# Create a data frame to hold the results
df_results = pd.DataFrame(df_dict)
# Calculate Accuracy
accuracy = accuracy_score(df_results.target, df_results.predicted)
# Print test loss and accuracy
print('Test Loss: {:.6f}\nTest Accuracy: {:.2%}'.format(test_loss.cpu().numpy(), accuracy))
return df_results
predictions_df = test_classes(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
predictions_df
predictions_count = predictions_df
predictions_count['count'] = 1
predictions_count = predictions_count.groupby(['target', 'predicted', 'result']).sum().reset_index()
predictions_count
# Plotting the target vs prediction for the final model
fig, ax = plt.subplots(figsize=(12, 5))
sns.scatterplot(data=predictions_count,
x='target',
y='predicted',
hue='result',
size='count',
palette=['#e54040', '#40e580'],
alpha=0.5)
sns.despine()
plt.title('Target Vs Predictions', fontdict={'fontsize': 18, 'fontweight':'bold'})
plt.xlabel('Target', fontdict={'fontsize': 14})
plt.ylabel('Prediction', fontdict={'fontsize': 14})
plt.legend(framealpha=0.5, bbox_to_anchor=(1, 1), loc='upper left');
grouped_df = predictions_df[['target', 'result']].groupby('target').mean()
grouped_df.columns = ['accuracy']
fig, ax = plt.subplots(figsize=(20, 6))
ax.plot(grouped_df, color='#00334e')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_title('Accuracy across classes', fontdict={'fontsize': 20, 'fontweight':'bold'})
ax.set_xlabel('Class number', fontdict={'fontsize': 14})
ax.set_ylabel('Accuracy', fontdict={'fontsize': 14})
ax.set_xticks(np.arange(0, 133, 5))
ax.set_xlim(0)
ax.set_ylim(0);
classes_accuracy = grouped_df
classes_accuracy.index = class_names
classes_accuracy
# Display the lowest 5 classes accuracy
classes_accuracy.sort_values('accuracy').head()
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
# list of class names by index, i.e. a name can be accessed like class_names[0]
class_names = [item[4:].replace("_", " ") for item in train_data.classes]
def predict_breed_transfer(img_path):
# load the image and return the predicted breed
# First load the image
img = Image.open(img_path) #.convert('RGB')
# Setup the normalizer
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# Setting up image preprocessor
img_transform = transforms.Compose([transforms.Resize((224, 224)), # Resize the image to 244x244
transforms.ToTensor(), # Convert the numpy array that contains the image into a tensor
normalizer]) # Apply the normalizer
# Apply the preprocessing to the image before passing it to our model
# and add a dummy axis as the model expect a batch of images not a single image
preproccessed_img = img_transform(img)[:3,:,:].unsqueeze(0)
# check if CUDA is available
use_cuda = torch.cuda.is_available()
# move imag to GPU if CUDA is available
if use_cuda:
preproccessed_img = preproccessed_img.cuda()
# Use VGG16 to predict the class of the image
pred = model_transfer(preproccessed_img)
# Move the prediction to cpu and convert it to numpy array and return the index of the class of highest probability
pred = pred.cpu().data.numpy().argmax()
return class_names[pred], pred # return class name and index
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!

dirs = os.listdir('data/dog_images/test')
breed_images = []
for dir in dirs:
files = os.listdir('data/dog_images/test/' + dir)
breed_images.append(os.path.join('data/dog_images/test/' + dir + '/', files[0]))
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def run_app(img_path):
## handle cases for a human face, dog, and neither
img = Image.open(img_path)
# Use dog_detector to see if there is a dog in the picture first
if dog_detector(img_path):
# get the dog breed
breed, index = predict_breed_transfer(img_path)
# Create the figure to display the picture
fig, ax = plt.subplots(figsize=(4, 4))
# Display the dog picture with it's breed
ax.imshow(img)
ax.set_title(f'Predicted dog breed is {breed}', fontdict={'fontsize':13})
ax.set_xticks([]); ax.set_yticks([])
elif face_detector(img_path) > 0:
breed, index = predict_breed_transfer(img_path)
# Create a figure for the 2 pics original and a pic for the same predicted breed
fig, ax = plt.subplots(figsize=(8, 4), ncols=2)
# Display the original image
ax[0].imshow(img)
ax[0].set_xticks([]); ax[0].set_yticks([])
ax[0].set_title('Original Image', fontdict={'fontsize':13})
# Display the predicted dog breed image
dog_image = Image.open(breed_images[index])
ax[1].imshow(dog_image)
ax[1].set_title(f'Predicted Dog: {breed}', fontdict={'fontsize':13})
ax[1].set_xticks([]); ax[1].set_yticks([])
fig.text(.2, 0, f'Hi there I just found that you look like this {breed} dog', fontdict={'fontsize':14})
else:
# Display the original picture
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(img)
ax.set_title(f'Sorry neither dog nor human were found here', fontdict={'fontsize':13, 'color':'#ff2020'})
ax.set_xticks([]); ax.set_yticks([])
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer:
Acutaly the output was worse but overall I really enjoyed this project and the output was funny and we can improve the application by doing the following.
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
human_idx = np.random.randint(0, len(human_files), 3)
dog_idx = np.random.randint(0, len(dog_files), 3)
## suggested code, below
for file in np.hstack((human_files[human_idx], dog_files[dog_idx])):
run_app(file)
test_images = np.array(glob("my_test/*"))
for image in test_images:
run_app(image)